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# Licensed under the Apache License, Version 2.0 (the "License");
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# https://www.apache.org/licenses/LICENSE-2.0
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"""🔲 License Plate Detection model."""
from typing import Any, Dict, Optional
import cv2
import numpy as np
from peekingduck.pipeline.nodes.abstract_node import AbstractNode
from peekingduck.pipeline.nodes.model.yolov4_license_plate import (
yolo_license_plate_model,
)
[docs]class Node(AbstractNode): # pylint: disable=too-few-public-methods
"""Initializes and uses YOLO model to infer bboxes from image frame.
This customized YOLO node is capable of detecting objects from a single
class (License Plate). It uses YOLOv4 by default and can be changed to
use YOLOv4-tiny if FPS is critical over accuracy.
Inputs:
|img_data|
Outputs:
|bboxes_data|
|bbox_labels_data|
|bbox_scores_data|
Configs:
model_type (:obj:`str`): **{"v4", "v4tiny"}, default="v4"**. |br|
Defines the type of YOLO model to be used.
weights_parent_dir (:obj:`Optional[str]`): **default = null**. |br|
Change the parent directory where weights will be stored by
replacing ``null`` with an absolute path to the desired directory.
iou_threshold (:obj:`float`): **[0, 1], default = 0.3**. |br|
Overlapping bounding boxes above the specified IoU (Intersection
over Union) threshold are discarded.
score_threshold (:obj:`float`): **[0, 1], default = 0.1**. |br|
Bounding box with confidence score less than the specified
confidence score threshold is discarded.
References:
YOLOv4: Optimal Speed and Accuracy of Object Detection:
https://arxiv.org/pdf/2004.10934v1.pdf
Model weights trained using pretrained weights from Darknet:
https://github.com/AlexeyAB/darknet
.. versionchanged:: 1.2.0 |br|
``yolo_iou_threshold`` is renamed to ``iou_threshold``. |br|
``yolo_score_threshold`` is renamed to ``score_threshold``.
"""
def __init__(self, config: Dict[str, Any] = None, **kwargs: Any) -> None:
super().__init__(config, node_path=__name__, **kwargs)
self.model = yolo_license_plate_model.YOLOLicensePlateModel(self.config)
def run(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Reads the image input and returns the bboxes of the specified
objects chosen to be detected.
Args:
inputs (dict): Dictionary of inputs with key "img".
Returns:
outputs (dict): bbox output in dictionary format with keys
"bboxes", "bbox_labels", and "bbox_scores".
"""
image = cv2.cvtColor(inputs["img"], cv2.COLOR_BGR2RGB)
bboxes, labels, scores = self.model.predict(image)
bboxes = np.clip(bboxes, 0, 1)
outputs = {
"bboxes": bboxes,
"bbox_labels": labels,
"bbox_scores": scores,
}
return outputs
def _get_config_types(self) -> Dict[str, Any]:
"""Returns dictionary mapping the node's config keys to respective types."""
return {
"iou_threshold": float,
"model_type": str,
"score_threshold": float,
"weights_parent_dir": Optional[str],
}